None of the ovarian tumor antigens that have been identified to date is sufficiently predictive of ovarian cancer to be useful as a diagnostic or screening test. We hypothesize that a multi-antigenic screen, perhaps in combination with other markers or risk factors, will prove to be sufficiently predictive to detect early stage ovarian cancer. This application describes a strategy to identify novel immunogenic gene products expressed in early stage ovarian cancer using the SEREX (Serologic screening of cDNA Expression libraries) technique. Our long-range goal is to establish a reliable serum-based assay for early detection of ovarian cancer. Our immediate objectives are to identify a large number of antigens present in women with ovarian cancer, many of which may be novel and to characterize the set of antigens found to be most prominent in ovarian cancer patients. To accomplish these objectives, we will pursue two Specific Aims: Specific Aim #1. To generate a multiplex of ovarian cancer-specific antigens. To accomplish this Specific Aim an exhaustive screening of a subtracted ovarian cancer cDNA library will be performed to isolate a panel of ovarian cancer-associated antigens. These antigens will be combined with ovarian cancer-associated antigens previously identified by other researchers. The antigens will be expressed in E. coli in a 6xHis-tagged format and will be individually immobilized on individually internally color-coded LUMINEX beads. A multiplex antigen array based on the LUMINEX technology will be generated and optimized for screening the patients' sera. Specific Aim #2. To perform antigen array screening to establish comprehensive antigenic profiles for ovarian cancer patients. To construct a clinical prediction rule to identify predictors of ovarian cancer. To accomplish this Specific Aim, the antigen multiplex array generated in Specific Aim 1 will be used to screen sera from several ovarian cancer patient and control populations. The data will be analyzed using newly developed multi-parametric analysis software, and sets of ovarian cancer-specific antigens with high predictiveness will be selected. Using current methods in the area of classification, the clinical and antigen data will be used to develop a classification rule that can be used to identify ovarian cancer patients. By the end of the proposed project, we expect to construct a test based on a panel of ovarian cancer-specific antigens that has the most promising characteristics to identify women with ovarian cancer.